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Prompt engineering standards and context engineering principles for AI agents based on Anthropic best practices. Covers clarity, structure, progressive discovery, and optimization for signal-to-noise ratio.
Passed 22 of 22 test cases with the skill, and 21 of 22 without it.
1 fixed21 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
Rewrite the following system instruction to improve agent compliance while keeping the same functional meaning: 'You should always make sure to validate the user input prior to executing any backend processing.' State the revised instruction directly.
| Checked | Without skill | With skill |
|---|---|---|
| Uses imperative command verb format without modal auxiliary verbs like should or might | Missed | Passed |
The instructions your model reads when the skill loads.
Context engineering = Curating optimal set of tokens during LLM inference
Primary Goal: Find smallest possible set of high-signal tokens that maximize desired outcomes
Use clear semantic sections:
test cases passed, vs 21 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.